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LLMS
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Gemini 2.5 Pro Excels at Video Understanding with AI Studio
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Gemini 2.5 Pro is incredible at video understanding, try posting a YouTube link into AI studio http://
ai.dev and asking it questions about the video. You will be amazed! -
Building a Private RAG Chat App with LangChain and Reflex
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Private RAG Chat App Build a local RAG chat application that prioritizes data privacy, using LangChain's orchestration with Reflex framework to create secure, context-aware conversations. Learn to build your private chat app https://
apideck.com/blog/building-
a-local-rag-chat-app-with-reflex-langchain-huggingface-and-ollama
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Powerful RAG Implementation for Company Annual Reports Analysis
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Winning RAG Solution A powerful RAG implementation that analyzes company annual reports through LangChain's framework. Features PDF parsing, multi-LLM support, and advanced retrieval for precise question-answering. Check out the solution: https://
github.com/IlyaRice/RAG-C
hallenge-2
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Social Media Agent UI Tutorial with LangChain and Notion
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Social Media Agent UI Tutorial Transform LangChain's Social Media Agent into a user-friendly web app with multiple content submission methods and Notion integration. Built with ExpressJS and AgentInbox UI for seamless progress monitoring. Check out the step-by-step guide
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Implicit Token Caching for Repeated Input Contexts Now Available
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Caching of tokens when you use the same input context repeatedly now happens implicitly, making things a lot easier to deal with.
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Unsloth AI: Fine-tune LLMs 2x Faster with 80% Less VRAM
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Fine-tune Qwen3, LLaMA 4, and Gemma 2x faster with 80% less VRAM! Unsloth AI is an open-source Python framework that accelerates and simplifies the fine-tuning of LLMs. 100% Open Source
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llama.cpp now fully compatible with Vision Language Models
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llama.cpp is now fully compatible with VLMs HUGE kudos to @ngxson from HF and to the @ggml_org team Here are a selection of pre-quantized models, ready to be used, from:
– @GoogleDeepMind Gemma
– @MistralAI Pixtral
– @Alibaba_Qwen VL
– @huggingface SmolVLM Give them a -
Evaluating LLM context handling via prompt engineering
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The Study Plan (Prompt #1) is so practical and shows how much context each model can actually hold.
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Diffusion Policies and Multimodal Image-Based Action Representations
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It would be the same math/algorithms. The paper I shared uses a diffusion policy. Images are actions. Eg if you generate a spectrogram image, you can decode it immediately to speech. Speech acts can do a lot! We tend to imagine 1D embeddings for all modalities, but 2D